An intelligent pattern recognition model for assessment of terrorists’ activities in Nigeria

Uduak David George *, Samuel Sunday Udoh and Okure Udo Obot

Department of Computer Science, University of Uyo, Nigeria. 
 
Research Article
International Journal of Science and Research Archive, 2023, 09(02), 231–244.
Article DOI: 10.30574/ijsra.2023.9.2.0516
Publication history: 
Received on 19 May 2023; revised on 08 July 2023; accepted on 11 July 2023
 
Abstract: 
Terrorism and its brutal tendencies constitute a major setback to the development process of the Nigerian economy leading to severe loss of lives, destruction of properties, and a decline of interest in investment by both local and foreign investors. Many models for assessment of terrorist’s activities lack the ability of learning from previous patterns in order to guide pre-emptive actions against future occurrences, and there are no established regional pattern of weaponry, types of attack, as well as types of victims of terrorists’ operations. This study seeks to build a robust intelligent model for recognizing several terrorists’ patterns in each of the six geo-political zones of Nigeria. A data set of 5,503 instances of terrorists’ activities in Nigeria was obtained and a pattern recognition model was built using Artificial Neural Networks (ANN) with 70%, 15%, and 15% data splits for training, validation, and testing respectively. A 10-10-6 ANN architecture was designed and trained using the scaled conjugate gradient backpropagation algorithm. The training was carried out using Matlab’s neural network pattern recognition toolkit. In order to numerically represent categorical data, a sort-order scheme was developed by the authors and utilized. The results showed average percentage scores for accuracy, precision, recall and F1-score as 99.89, 99.96, 100 and 99.98 respectively. This showed acceptable performance. The developed model is therefore considered a robust one for recognition of terrorists’ patterns in Nigeria. This would assist security agencies to deal with terrorists’ incidences with high intelligent information and advanced preparation for prevention and control. The developed model is highly recommended for use by the security agencies in the country.
 
Keywords: 
Terrorism; Terrorists’ pattern; Neural network; Pattern recognition; Cross-entropy; Geo-political zones
 
Full text article in PDF: 
 
Copyright information: 

Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0